1. Identification of furin protease small-molecule inhibitor with a 1,3-thiazol-2-ylaminosulfonyl scaffoldAnja Kolarič, Vid Ravnik, Sara Štumpf Horvat, Marko Jukič, Urban Bren, 2025, original scientific article Keywords: computer-assisted drug design, CADD, computer-assisted drug design, furin inhibitors, protease inhibitors, antivirals, protease inhibitors, furin assay, antiviral drug design
Published in DKUM: 20.03.2025; Views: 0; Downloads: 0
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2. Študija in optimizacija vezave antikalinov na male molekule : diplomsko delo univerzitetnega študijskega programa I. stopnjeAlja Špec, 2024, undergraduate thesis Abstract: V diplomskem delu smo raziskovali optimizacijo vezave anitkalina na majhno molekulo s tehniko molekulskega sidranja in in silico mutageneze. Osredotočili smo se na razumevanje, kako lahko specifične mutacije aminokislin znotraj antikalina vplivajo na njegove interakcije z različnimi ligandi. Pri tem smo s programskim jezikom Python pripravili skripto, ki nam je pomagala pri izvedbi in silico mutageneze aminokislinskih ostankov in oceno vezave med proteinom in ligandom. Rezultati so pokazali potencialno ugodne mutacije, ki bi lahko povečale vezavno afiniteto liganda. Tako smo pokazali enostavni postopek, s katerim lahko optimiziramo vezavo testnih ligandov na antikaline in s tem skrajšamo in vitro postopke oz. efektivneje izvajamo laboratorijsko delo. Ugotovitve diplomske naloge pa prispevajo tudi dragocen vpogled v podrobnosti interakcij ligand-protein, saj lahko vezavno mesto antikalinov omogoči visoko selektivnost za vezavo preučevanih malih molekul.
Keywords: in silico mutageneza, antikalini, molekulsko sidranje, lipokalinske strukture, vezavno mesto
Published in DKUM: 15.07.2024; Views: 132; Downloads: 50
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3. Primerjava proteomov glavnih predstavnikov virusov iz družine Filoviridae in identifikacija potencialnih vezavnih mest za načrtovanje ligandov : magistrsko deloKatja Gole, 2024, master's thesis Abstract: Virus Ebola Zaire (EBOV) in virus Marburg Marburg (MARV) sta glavna predstavnika družine Filoviridae, ki pri ljudeh povzročata hudo virusno hemoragično mrzlico, s stopnjo smrtnosti do 90 %. Kljub temu je zaradi le občasnih izbruhov bolezni, ki jih ni možno predvideti, razvoj zdravil okrnjen in se izvaja predvsem v akademskih krogih. V magistrskem delu smo preverjali podobnost virusnih proteomov in vezavnih mest z namenom, da bi v prihodnje bilo možno načrtovati učinkovine, ki bi se lahko vezale na terapevtske tarče obeh virusov. EBOV in MARV kodirata enakih sedem proteinov, in sicer NP, VP35, VP40, GP, VP30, VP24 in L protein, ki imajo glede na informacije, ki smo jih pridobili s pomočjo podatkovnih zbirk UniProt in PDB, podobne funkcije in zgradbo. Podobnost proteomov smo potrdili še z BLAST analizo aminokislinskih sekvenc med posameznimi proteini obeh virusov, pri čemer se podobnost proteinov giblje med 44 in 55 %, oziroma 68 % v 1. delu sekvence L proteinov. S programom ProBiS smo glede na primerjavo z že znanimi strukturami podobnih proteinov določili potencialna vezavna mesta in pripadajoče potencialne ligande. Vezavna mesta smo opisali in jih primerjali s pomočjo superpozicije proteinov obeh virusov. Kot rezultat smo pridobili tri pare podobnih vezavnih mest, ki se kljub podobnosti razlikujejo v posameznih aminokislinah, kar smo dokazali z BLAST primerjavo. S PLIP analizo smo pokazali, da aminokislinski ostanki v vezavnem mestu posameznega virusa tvorijo podobne interakcije z enakimi ligandi in da s tem obstaja možnost načrtovanja terapevtikov, ki bi se hkrati vezali na tarče EBOV in MARV.
Keywords: Ebola, Marburg, BLAST, vezavna mesta, ProBiS, načrtovanje ligandov
Published in DKUM: 11.07.2024; Views: 104; Downloads: 22
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4. Molecular Filters in Medicinal ChemistrySebastjan Kralj, Marko Jukič, Urban Bren, 2023, review article Keywords: medicinal chemistry, filtering chemical libraries, chemical space, HTVS, virtual screening, computer aided drug-design, in silico drug design, bioinformatics, chemoinformatic, compound library
Published in DKUM: 20.05.2024; Views: 168; Downloads: 16
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6. Mechanistic insights of polyphenolic compounds from rosemary bound to their protein targets obtained by molecular dynamics simulations and free-energy calculationsSamo Lešnik, Marko Jukič, Urban Bren, 2023, original scientific article Keywords: rosemary, carnosic acid, carnosol, rosmanol, rosmarinic acid, polyphenols, molecular docking, molecular dynamics simulations, linear interaction energy calculations, water-mediated hydrogen-bonds, HIV-1 protease, K-RAS protein, factor X
Published in DKUM: 22.04.2024; Views: 185; Downloads: 24
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7. Conserved water networks identification for drug design using density clustering approaches on positional and orientational dataJelena Tošović, Domagoj Fijan, Marko Jukič, Urban Bren, 2022, original scientific article Keywords: cluster chemistry, hydrogen, ligands, molecules, oxygen
Published in DKUM: 10.04.2024; Views: 221; Downloads: 15
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8. Identification of triazolopyrimidinyl scaffold SARS-CoV-2 papain-like protease (PLpro) inhibitorSebastjan Kralj, Marko Jukič, Miha Bahun, Luka Krajnc, Anja Kolarič, Milan Hodošček, Nataša Poklar Ulrih, Urban Bren, 2024, original scientific article Keywords: drug design, protease inhibitor, SARS-CoV-2, papain-like protease, PLpro, antiviral design, in silico drug design, CADD, virtual screening, HTVS, structure-based design
Published in DKUM: 26.01.2024; Views: 358; Downloads: 23
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9. Design of Tetra-Peptide Ligands of Antibody Fc Regions Using In Silico Combinatorial Library ScreeningMarko Jukič, Sebastjan Kralj, Anja Kolarič, Urban Bren, 2023, original scientific article Abstract: Abstract
Peptides, or short chains of amino-acid residues, are becoming increasingly important as active ingredients of drugs and as crucial probes and/or tools in medical, biotechnological, and pharmaceutical research. Situated at the interface between small molecules and larger macromolecular systems, they pose a difficult challenge for computational methods. We report an in silico peptide library generation and prioritization workflow using CmDock for identifying tetrapeptide ligands that bind to Fc regions of antibodies that is analogous to known in vitro recombinant peptide libraries’ display and expression systems. The results of our in silico study are in accordance with existing scientific literature on in vitro peptides that bind to antibody Fc regions. In addition, we postulate an evolving in silico library design workflow that will help circumvent the combinatorial problem of in vitro comprehensive peptide libraries by focusing on peptide subunits that exhibit favorable interaction profiles in initial in silico peptide generation and testing.
Keywords: peptide design, in silico combinatorial library, peptide combinatorial library, peptide library design, high-throughput virtual screening, peptide molecular docking, antibody purification, peptide drug design, recombinant peptide libraries
Published in DKUM: 01.12.2023; Views: 353; Downloads: 83
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10. Naive prediction of protein backbone phi and psi dihedral angles using deep learningMatic Broz, Marko Jukič, Urban Bren, 2023, original scientific article Abstract: Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper presents data acquisition, deep learning model definition, and training toward the final protein backbone angle prediction. The method applies a simple fully connected neural network (FCNN) model that takes only the primary structure of the protein with a sliding window of size 21 as input to predict protein backbone φ and ψ dihedral angles. Despite its simplicity, the model shows surprising accuracy for the φ angle prediction and somewhat lower accuracy for the ψ angle prediction. Moreover, this study demonstrates that protein secondary structure prediction is also possible with simple neural networks that take in only the protein amino-acid residue sequence, but more complex models are required for higher accuracies.
Keywords: protein structure prediction, backbone dihedral angles, deep neural network, fully connected neural network, FCNN, protein secondary structure prediction
Published in DKUM: 01.12.2023; Views: 421; Downloads: 166
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